Mapping the research landscape of behavioural finance and artificial intelligence in retail stock market investment

A bibliometric and thematic review (2020–2026)

Authors

  • Shilpi Jauhari Institute of Engineering and Technology, Lucknow, India
  • S M Qasim Institute of Engineering and Technology, Lucknow, India
  • Raji Kaur Institute of Engineering and Technology, Lucknow, India
  • Harshmit Kaur Saluja Institute of Engineering and Technology, Lucknow, India

Keywords:

Behavioural Biases, Artificial Intelligence, Retail Investors, Bibliometric Analysis, Thematic Review, Investment Decision-Making, Fintech, Machine Learning

Abstract

Retail investors’ decisions increasingly reflect a complex interplay between psychological biases and technology-driven tools, particularly Artificial Intelligence (AI), in contemporary financial markets. This bibliometric and thematic review maps the evolving intellectual landscape at the intersection of behavioural biases, Artificial Intelligence (AI), and retail investment decision-making. Using 428 English-language, peer-reviewed articles indexed in Scopus and Web of Science from 2020 to 2026, the study applies the bibliometrix R package and the PRISMA framework to ensure systematic screening and analysis. The findings indicate a strong upward growth trajectory, with nearly 74% of publications appearing between 2024 and 2026, reflecting the rapid expansion of scholarly interest in this area. The publication landscape is concentrated in a limited number of influential journals, with IEEE Access and the Journal of Behavioral and Experimental Finance emerging as key sources and Author-level analysis also reveals a small group of highly influential researchers. Geographically, research output is dominated by China, India, and the United States, with limited international collaboration. The thematic structure identifies investments, financial markets, machine learning, and forecasting as core themes, while artificial intelligence, fintech, and behavioural finance are emerging areas. Overall, the study shows a clear shift from fintech-oriented research toward a more AI-driven agenda, with growing attention to investor sentiment, financial stability, algorithmic trading, and natural language processing.

References

Aksnes, D. W., & Sivertsen, G. (2019). A criteria-based assessment of the coverage of Scopus and Web of Science. Journal of Data and Information Science, 4(1). DOI: 10.2478/jdis-2019-0001

Annu, & Tripathi, R. (2023). A bibliometric and systematic review analysis of adopting decision intelligence analytics for rational decision-making. Qualitative Research in Financial Markets, 16(5), 915–936. https://doi.org/10.1108/QRFM-01-2023-0005

Annu, & Tripathi, R. (2025). An insight of financial literacy and artificial intelligence to mitigate behavioral biases: A bibliometric and systematic review analysis using SPAR-4-SLR. International Journal of Emerging Markets, 20(12), 5047–5068. https://doi.org/10.1108/IJOEM-07-2024-1110

Archambault, É., Vignola-Gagné, É., Côté, G., Larivière, V., & Gingras, Y. (2006). Benchmarking scientific output in the social sciences and humanities: The limits of existing databases. Scientometrics, 68(3), 329–342. DOI: 10.1007/s11192-006-0115-z

Aria, M., & Cuccurullo, C. (2017). Bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. https://doi.org/10.1016/j.joi.2017.08.007

Aria, M., Cuccurullo, C., D’Aniello, L., & Spano, M. (2026). Biblioshiny and the SAAS workflow: An integrated framework for transparent and reproducible science mapping. A demonstration through the replication of a study. Journal of Informetrics, 20(3), 101837. https://doi.org/10.1016/j.joi.2026.101837

Atkins, A., Niranjan, M., & Gerding, E. (2018). Financial news predicts stock market volatility better than close price. The Journal of Finance and Data Science, 4(2), 120–137. https://doi.org/10.1016/j.jfds.2018.02.002

Baas, J., Schotten, M., Plume, A., Côté, G., & Karimi, R. (2020). Scopus as a curated, high-quality bibliometric data source for academic research in quantitative science studies. Quantitative Science Studies, 1(1), 377–386. 10.1162/qss_a_00019

Barberis, N., & Thaler, R. H. (2003). A survey of behavioral finance. In G. M. Constantinides, M. Harris, & R. M. Stulz (Eds.), Handbook of the economics of finance. 1, 1053–1128. Elsevier. https://doi.org/10.1016/S1574-0102(03)01027-6

Broadus, R. N. (1987). Toward a definition of “bibliometrics.” Scientometrics, 12(5–6), 373–379. https://doi.org/10.1007/BF02016680

Chansanam, W., & Li, C. (2025). KKU-BiblioMerge. Iberoamerican Journal of Science Measurement and Communication, 5(1), 1–16. 10.47909/ijsmc.157

Chen, Y., Pelger, M., & Zhu, J. (2021). Deep learning in asset pricing. The Review of Financial Studies, 34(7), 3206–3243. 10.2139/ssrn.3350138

Cheng., & T. Tsung. (2016). The congruity between linkage-based factors and content-based clusters—an experimental study using multiple document corpora. Journal of the Association for Information Science and Technology, 67(3),610–619 https://doi.org/10.1002/asi.23413

Cobo, M. J., López-Herrera, A. G., Herrera-Viedma, E., & Herrera, F. (2011a). An approach for detecting, quantifying, and visualizing the evolution of a research field: A practical application to the Fuzzy Sets Theory field. Journal of Informetrics, 5(1), 146–166. 10.1016/j.joi.2010.10.002

Cobo, M. J., López-Herrera, A. G., Herrera-Viedma, E., & Herrera, F. (2011b). Science mapping software tools: Review, analysis, and cooperative study among tools. Journal of the American Society for Information Science and Technology, 62(7), 1382–1402. 10.1002/asi.21525

Cobo, M. J., Martínez, M. A., Gutiérrez-Salcedo, M., Fujita, H., & Herrera-Viedma, E. (2015). 25 years at Knowledge-Based Systems: A bibliometric analysis. Knowledge-Based Systems, 80, 3–13. https://doi.org/10.1016/j.knosys.2014.12.035

Dagli, N., Kurian, J. R., & Haque, M. (2025). A perspective on merging databases. Bangladesh Journal of Medical Science, 24(4), 1025-1028. https://doi.org/10.3329/bjms.v24i4.84667

Diez-Junguitu, D., & Peña-Cerezo, M. Á. (2026). BiblioMerge: A Python-based automated tool to merge WoS and Scopus bibliographic data, compatible with Biblioshiny, BibExcel, VOSviewer, SciMAT and ScientoPy. Journal of Informetrics, 20(1), 101770. https://doi.org/10.1016/j.joi.2026.101770

Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285–296. 10.1016/j.jbusres.2021.04.070

Echchakoui, S. (2020). Merging Scopus and Web of Science during bibliometric analysis: the case of sales force literature from 1912 to 2019. Journal of Marketing Analytics, 8, 165–184. 10.1057/s41270-020-00081-9

Fischer, T., & Krauss, C. (2018). Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research, 270(2), 654–669. 10.1016/j.ejor.2017.11.054

Gandhmal, D. P., & Kumar, K. (2019). Systematic analysis and review of stock market prediction techniques. Computer Science Review, 34, 100190. https://doi.org/10.1016/j.cosrev.2019.08.001

Glanzel, W., Moed, H.F., Schmoch, U., and Thelwall, M. (2019). Springer Handbook of Science and Technology Indicators. Springer.com

Gupta, S., & Rao, V. S. (2026). Behavioural biases in AI-assisted investment decisions: Evidence from young retail investors. International Journal of Economic Perspective, 20(7), 1–13.

Gzoyan, E., Sivertsen, G., & Sargsyan, S. (2026). The scientific landscape in the South Caucasus: A comparative analysis of Armenia, Azerbaijan, and Georgia (2012–2024). Scientometrics, 131, 2025–2047. https://doi.org/10.1007/s11192-026-05593-2

Haddaway, N. R., Page, M. J., Pritchard, C. C., & McGuinness, L. A. (2022). PRISMA2020: An R package and Shiny app for producing PRISMA 2020-compliant flow diagrams, with interactivity for optimised digital transparency and Open Synthesis. Campbell Systematic Reviews, 18(2), e1230. https://doi.org/10.1002/cl2.1230

Hensel, P. G. (2025). Scientific software as geopolitics: Evidence of national attribution bias from a large-scale replication in bibliometrics. Scientometrics, 130, 6135–6152. https://doi.org/10.1007/s11192-025-05466-0

Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263–291. https://doi.org/10.2307/1914185

Kara, B. C., Şahin, A., & Dirsehan, T. (2025). BibexPy: Harmonizing the bibliometric symphony of Scopus and Web of Science. SoftwareX, 30, 102098. https://doi.org/10.1016/j.softx.2025.102098

Katenova, M., Frolova, Y., & Schmunkamp, P. (2025). Artificial intelligence in financial behavior: Bibliometric ideas and new opportunities. Journal of Risk and Financial Management, 18(3), 159. 10.3390/jrfm18030159

Khan, A., Goodell, J. W., Hassan, M. K., & Paltrinieri, A. (2022). A bibliometric review of finance bibliometric papers. Finance Research Letters, 47, 102520. https://doi.org/10.1016/j.frl.2021.102520

Khan, W., Ghazanfar, M. A., Azam, M. A., Karami, A., Alyoubi, K. H., & Alfakeeh, A. S. (2022). Stock market prediction using machine learning classifiers and social media, news. Journal of Ambient Intelligence and Humanized Computing, 13(7), 3433–3456. https://doi.org/10.1007/s12652-020-01839-w

Kulkarni, M. S., Patil, K. P., & Pramod, D. (2025). The role of robo-advisors in behavioural finance, shaping investment decisions. Cogent Economics & Finance, 13(1), 2571403. 10.1080/23322039.2025.2571403

Kumar, D., Sarangi, P. K., & Verma, R. (2022). A systematic review of stock market prediction using machine learning and statistical techniques. Materials Today: Proceedings, 49(8), 3187–3191. https://doi.org/10.1016/j.matpr.2020.11.399

Kumpulainen, M., & Seppänen, M. (2022). Combining Web of Science and Scopus datasets in citation-based literature study. Scientometrics, 127, 5613–5631. 10.1007/s11192-022-04475-7

Lim, J. S., & Zhang, J. (2022). Adoption of AI-driven personalization in digital news platforms: An integrative model of technology acceptance and perceived contingency. Technology in Society, 69, 101965. https://doi.org/10.1016/j.techsoc.2022.101965

Ma, D., Yuan, D., & Dong, L. (2024). VGC-GAN: A multi-graph convolution adversarial network for stock price prediction. Expert Systems with Applications, 236, 121204. https://doi.org/10.1016/j.eswa.2023.121204

Mishra, A., & Tripathi, V. (2025). From mobile trading to intelligent investing: A bibliometric and thematic review of AI-driven financial investments. Economic Sciences: Theory and Practice, 83(1), 107–131. 10.30546/jestp.2026.85.01.0131

Mongeon, P., & Paul-Hus, A. (2016). The Journal coverage of Web of Science and Scopus: a comparative analysis. Scientometrics, 106, 213–228. 10.1007/s11192-015-1765-5

Nassirtoussi, A. K., Aghabozorgi, S., Wah, T. Y., & Ngo, D. C. L. (2014). Text mining for market prediction: A systematic review. Expert Systems with Applications, 41(16), 7653–7670. https://doi.org/10.1016/j.eswa.2014.06.009

NOAA Library (2019). Bibliometrics and Research Evaluation: Network Analysis.

Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., McGuinness, L. A., Stewart, L. A., Thomas, J., Tricco, A. C., Welch, V. A., Whiting, P., & Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. The BMJ, 372, n71. https://doi.org/10.1136/bmj.n71

Phan, T. A., Tran, H. P., Ninh, T. T., & Nguyen, H. K. (2026). Mind over machine: A bibliometric journey into investor perceptions of AI in stock markets. Strategic Business Research, 2(1), 100115. https://doi.org/10.1016/j.sbr.2026.100115

Pritchard, A. (1969). Statistical bibliography or bibliometrics. Journal of Documentation, 25, 348.

Scientometrics (2020). Bibliometric and network indicators for characterizing research communities. Springer.

Sharma, S., & Negi, V. (2025). Effect of behavioural biases on investors’ decision making: A systematic literature review. Metamorphosis: A Journal of Management Research, 24(1), 105–113. https://doi.org/10.1177/09726225251319119

Simonn, F. C. (2025). Past, present, and future research trajectories on retail investor behaviour: A composite bibliometric analysis and literature review. International Journal of Financial Studies, 13(2), 105. https://doi.org/10.3390/ijfs13020105

Tao, Z., Wu, W., & Wang, J. (2024). Series decomposition Transformer with period-correlation for stock market index prediction. Expert Systems with Applications, 237, 121424. https://doi.org/10.1016/j.eswa.2023.121424

Wang, X., Fang, Z., & Sun, X. (2016). Usage patterns of scholarly articles on Web of Science: A study on Web of Science usage count. Scientometrics, 109(2), 917–926. https://doi.org/10.1007/s11192-016-2093-0

White, H. D., & Griffith, B. C. (1981). Author cocitation: A literature measure of intellectual structure. Journal of the American Society for Information Science, 32(3), 163–171. 10.1002/asi.4630320302

Yadav, S., & Kant, R. (2026). Enhancing investor rationality: A review of behavioural biases and the de-biasing potential of artificial intelligence in investment decision making. Asian Journal of Business and Accounting, 26(4), 190–203. 10.9734/ajeba/2026/v26i42232

Zupic, I., & Čater, T. (2015). Bibliometric methods in management and organization. Organizational Research Methods, 18(3), 429–472. https://doi.org/10.1177/1094428114562629

Published

2026-11-02

How to Cite

Jauhari, S., Qasim, S. M., Kaur, R., & Saluja, H. K. (2026). Mapping the research landscape of behavioural finance and artificial intelligence in retail stock market investment: A bibliometric and thematic review (2020–2026). International Journal of Economic Perspectives, 20(11), 1083–1116. Retrieved from https://ijeponline.lingcure.org/index.php/journal/article/view/1402

Issue

Vol. 20 No. 11 (2026)

Section

Peer Review Articles
Copyright & Licensing

Copyright (c) 2026 Shilpi Jauhari, S M Qasim, Raji Kaur, Harshmit Kaur Saluja

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Allows users to: distribute and copy the article; create extracts, abstracts, and other revised versions, adaptations or derivative works of or from an article (such as a translation); include in a collective work (such as an anthology); and text or data mine the article. These uses are permitted even for commercial purposes, provided the user: gives appropriate credit to the author(s) (with a link to the formal publication through the relevant URL ID); includes a link to the license; indicates if changes were made; and does not represent the author(s) as endorsing the adaptation of the article or modify the article in such a way as to damage the authors' honor or reputation. CC BY